It is important in many security, surveillance, sports and other applications to be able to detect patterns of activities from an event associated with the application such that someone observing the patterns can draw useful insight about the activities and about those individuals participating in the activities. To date, this has typically only been possible via manual review and analysis of live or recorded video that captures the event activity.
While not the only application in which this need is important, sporting events provide a good illustration of such a need. It is known that sporting events are the most popular form of remote live entertainment in the world, attracting millions of viewers on television, personal computers, and a variety of other devices. Sports have an established and sophisticated broadcast production process involving producers, directors, commentators, analysts, and a sizeable crew of video and audio technicians using numerous cameras and microphones.
In recent years, computer-generated visualizations have been increasingly used in sports production to further enhance the viewer experience. Interactive visualization is becoming even more important with the ongoing convergence of television and Internet broadcasting. These trends have led to a significant amount of work on sports analysis, visualization, and interactive video browsing.
Most of the existing work related to sports visualization falls into two categories, which attempt to significantly enhance the experience of the event: (1) overlay of virtual objects over video using augmented reality techniques (examples of these include the virtual first-down line in football or the virtual offside line in soccer); and (2) generation of virtual renderings of action.
However, the emphasis of these existing visualization techniques has primarily been on re-synthesizing the sport, and not on deeper analysis of the sport. As mentioned above, the sports viewer, commentator, analyst, player, or coach is often trying to obtain further insight into performance, style, and ultimately strategy. In the context of a security or surveillance application, an observer gains insight by attempting to detect security problems/situations by viewing individuals, objects and activities associated with the live or recorded video feed that he/she is observing. Unfortunately, such ability to gain insight into aspects of an event is not possible with existing techniques and systems.
Thus, there exists a need for techniques that overcome the above mentioned drawbacks by providing automated performance data mining associated with a domain-specific event based on real time analysis of sensor data captured in accordance with the domain-specific event.